Pre-trained Language Model Based Active Learning for Sentence Matching
This work addresses annotation efficiency for natural language processing tasks, offering an incremental improvement over existing entropy-based methods.
The paper tackles the problem of reducing annotation costs in sentence matching by proposing a pre-trained language model-based active learning approach that incorporates linguistic criteria, achieving greater accuracy with fewer labeled training instances.
Active learning is able to significantly reduce the annotation cost for data-driven techniques. However, previous active learning approaches for natural language processing mainly depend on the entropy-based uncertainty criterion, and ignore the characteristics of natural language. In this paper, we propose a pre-trained language model based active learning approach for sentence matching. Differing from previous active learning, it can provide linguistic criteria to measure instances and help select more efficient instances for annotation. Experiments demonstrate our approach can achieve greater accuracy with fewer labeled training instances.